The identification of contiguous regions of a structure or organ in data from a diagnostic imaging system is known as segmentation. The automatic segmentation of structures is a technically difficult issue that has become even more problematic with the introduction of higher resolution diagnostic imaging systems.
Segmentation algorithms typically involve attempting to segregate a specific structure of a patient's anatomy based on a parameter such as a Hounsfield number for an image acquired with a computed tomography system or a proton density for an image acquired with a magnetic resonance imaging system. Using an image acquired with a computed tomography system as an example, a segmentation algorithm will typically identify areas to be segmented by Hounsfield number and then perform a connected component analysis in order to group these areas into a segmented volume. However, as diagnostic imaging systems have increased in resolution, the resulting image data often contain an increased level of noise. Since traditional segmentation algorithms cannot discriminate between accurate data and data that are corrupted by noise, the segmentation algorithm may introduce some regions of corruption when generating a segmentation mask. The regions of corruption typically show up as diffused spatial regions, or leakage regions, within the segmentation mask
The issue of accurately segmenting a specific structure of the patient is particularly problematic when the structure includes a narrow passageway with a low signal such as an airway within the patient's lungs or a vessel. Because modern diagnostic imaging systems provide higher resolution data, it is possible to segment small-diameter airways and vessels. However, when the segmentation algorithm is applied to the data, it is quite common that the segmentation algorithm will erroneously include areas outside of the airway or vessel in the segmented volume.
For these and other reasons, there is a need for a method to improve the segmented volume from three-dimensional diagnostic imaging data.